Ten Simple Rules for Using a Raspberry Pi
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Much of biology (and, indeed, all of science) is fast becoming computational. While this has been clear for some time (decades) from the perspectives of algorithms and software, a shift is also becoming more pervasive from the viewpoint of hardware and computer engineering. As examples, (i) on the informatics side of computational biology, recent years have seen field-programmable gate arrays (FPGAs) used to accelerate sequence alignment, and (ii) on the physics-based side of computational biology, application-specific integrated circuits (ASICs) and general-purpose GPU (GPGPU) computing have enabled the recent revolution in long-timescale molecular dynamics simulations. These trends notwithstanding, science students often receive little to no training in the more technical and hardware-focused areas of computational sciences (e.g., Linux/Unix usage, device drivers, kernel modules and so on). Just a few weeks with a Raspberry Pi would begin to remedy that. A major goal of our piece is to motivate the reader to become fearless in striving for a basic familiarity of these sorts of self-taught, do-it-yourself approaches to computing (and, mostly, to have fun doing it!). To quote Feynman, "What I cannot create, I do not understand".
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